Advanced driver assist systems (ADAS) in vehicles range from cruise control and adaptive lighting to more advanced systems such as automatic braking, automatic steering to keep the vehicle in the right lane, or alerting the driver of the presence of other cars, etc. Generally, ADAS retrieve input data from multiple sources such as image processing from cameras, radar, or LiDAR. More recently, vehicle-to-vehicle connectivity is arising as a promising addition to present ADAS.
Path prediction is an important component for advanced driver assistance systems for proving the vehicle with a safe and natural behavior in the traffic. Path prediction methods have to robustly take into account the available information relating to the vehicles present path and also to account for the un-predictable behavior of the other road users.
The two main machine learning paradigms for ADAS are end-to-end learning and modular system design. In end-to-end learning, one machine learning system will be used to control the vehicle by observing sensor data such as images from camera or data from a radar. In the modular system design, perception and control parts are isolated and the intermediate outputs from the perception are analyzed separately to get the required understanding of the environment which is then used in the decision and control modules to provide an input for the steering wheel and gas/brake pedals.
Although the above methods provide promising path prediction solutions for taking appropriate driving decisions, there is still appears to be room for improvement.